time-series batch / whole-series feature calculation

[jonasvdd]

Objectives:

Functionality

• [ ] convenient way to extract features over the whole, unsegmented data (see also #67)
  • [ ] Discuss + decide together with @jvdd @mbignotti what option seems best to serve this functionality (regarding end user perspective)

Available options:

1. introduce a new method to the FeatureCollection (as done here):
   • advantages ✅
     • Explicit method definition, less confusion for end-users
   • disadvanges :x:
     • A new method is introduced / less uniform interface to perform computation
2. Perform unsegmented feature computation when all window and/or stride are NOT set.
   • advantages ✅
     • more homogenous interface
   • disadvanges :x:
     • somewhat more implicitness code example:

 # NOTE: window and stride parameters are omitted. 
fc = FeatureCollection(
    FeatureDescriptor(
        function = np.mean,
        series_name="Value",
    )
)

# Uses the whole (unsegmented) series of `data` to 
# calculate the features. method remains the same.
fc.calculate(data=df, return_df=True)

3. Perform unsegmented feature computation when all windows are set to -1
4. a combination of (2.) and (3.)

As for now, this is performed by introducing the calculate_unsegmented method to the FeatureCollection:

Bug fixes

• [ ] fix the window_idx="end" and (window-size > data-range) bug